Heuristic Algorithm for Uncertain Permutation Flow-shop Problem

Abstract

A complex population-based solution algorithm for an uncertain decision making problem is presented. The uncertain version of a permutation flow-shop problem with interval execution times is considered. The worst-case regret based on the makespan is used for the evaluation of permutations of tasks. The resulting complex minmax combinatorial optimization problem is solved. The heuristic algorithm is proposed which is based on the decomposition of the problem into three sequential sub-problems and employs a paradigm of evolutionary computing. The proposed algorithm solves the sub-problems sequentially. It is compared with the fast middle point heuristic algorithm via computer simulation experiments. The results show the usefulness of this heuristic algorithm for instances up to five machines.